The invention relates to a time series prediction system for financial securities utilizing Artificial Neural Network (ANN). More particularly, the invention relates to a processing system based on the recurrent ANN architecture capable of outputting upper and lower prediction bounds at any given confidence, which is based on the validation errors of the ANN. Specifically, the invention relates a prediction system that can be applied to any financial time series which can be called by any computer language and Web applications supporting the system.
In the recent past, Recurrent Artificial Neural Networks have successfully improved the quality of forecasting of share movements in relation to its statistically based counterparts. The known recurrent neural networks (RNN) make a prediction of the appreciation potential of each stock based on the available historical data. The training process continues until at least one stopping criterion is met. Such criteria include the determination that the connections between the nodes of the net have reached a steady state, that the error between the predicted output and the actual target values is less than a certain threshold, or that a predefined time period has elapsed without any improvement in the net""s performance. Once the neural nets for each stock of the capital market have been trained and tested on the available historical data the neural nets are tied to surpass the underlying market benchmark by predicting, the task becomes one of holding a smaller subset of all stocks of the market, such that this subset has a higher expected return and about the same level of risk as the market index. Such a task requires one to focus on individual stocks and their performance in relation to the index that serves as the underlying performance benchmark.
Individual stocks usually have their own unique performance characteristics some of which can be quantified. Clearly, however, the relationships among such data are complicated and frequently non-linear, making them difficult to analyze. In summary, an investment decision in the modern capital markets requires processing of large volumes of data and taking into account a number of factors which may exhibit significant non-linear relationships among different components of the data.
Computers, in general, are very adept at dealing with large amounts of numerical information. However, sophisticated techniques are required to analyze and combine disparate information that can potentially impact security prices. Several expert computer systems have been deployed in the domain of finance, including some in the area of investment management.
In the past several years, recurrent neural networks (RNN) have become popular in solving a variety of problems. Neural nets mimic the ability of the human brain to recognize recurring patterns that are not just identical but similar. A neural net can predict the value of one variable based on input from several other variables that can impact it. The prediction is made on the basis of an inventory of patterns previously learned, seeking the most relevant in a particular situation. In summary, RNNs can xe2x80x9clearnxe2x80x9d by example and modify themselves by adjusting and adapting to changing conditions. Several applications of neural nets to the domain of finance are already known in the art. Typically, the RNN prediction systems are xe2x80x9cselfxe2x80x9d trained by adjusting weights and biases as a result of numerous repetitions. What the known systems typically do not do is to calculate an error function so the system""s output can be adjusted or controlled in accordance with the determined error.
U.S. Pat. No. 5,109,475 to Kosaka et al. discloses a neural network for selection of time series data. This process is illustrated in a particular application to the problem of stock portfolio selection. In the first step of the proposed process, certain characteristics for each security are calculated from time series data related to the security. The characteristics to be computed include the historical risk (variance and co-variance) and the return. The following step involves the establishment of a performance function based on the calculated characteristics and, in the third step of the process, a Hopfield neural network is used to select a subset of securities from a predefined universe. Due to the fact that the Kosaka system only considers historical risk and return data, and implicitly assumes that the relationship between risk and return factors will remain stable in the future, in a typical rapidly changing market environment, it is unlikely to predict accurately price variations which are subject to complicated non-linear relationships.
U.K Pat. application 2 253 081 A to Hatano et al. discloses a neural net for stock selection using only price data as input. The main idea of the proposed system is to calculate runs (sequences) of price trends, increases and decreases, using a point-and-figure chart and using the maximum and minimum values from the chart to make a time-series prediction using a neural network. As in the previous case, the Hatano system only uses historic price data which places limitation on the type and quality of predictions that may be achieved. Additionally, the use of only the external points of the price chart obscures even further information about any time dependencies that might be present in the original data.
The above-described financial systems do not fully utilize the potential of the neural nets for stock selection. Notably missing is the possibility to develop the standard adaptive training procedure of the RNN to determine a prediction error or function in accordance to which the RNN output can be controlled. Further, many of the known investment management systems have not been able to effectively output the upper and lower error bounds at a given confidence level. Further, the movements of the stock prices, as well as price movements of other financial instruments, generally present a deterministic trend superimposed with some xe2x80x9cnoisexe2x80x9d signals, which are, in turn, composed of truly random and chaotic signals, as illustrated in FIG. 1. Deterministic trends can be detected and assessed by some maximum-likelihood processes. Although a truly random signal, often represented by a Brownian motion, is unpredictable, it can be estimated by its mean and standard deviation. The chaotic signal, seemingly random but with deterministic nature, proves predictable to some degree by means of several analysis techniques, among which the ANN techniques have proven most effective over the widest range of predictive variables. However, this trend is largely ignored by the above-discussed references. As a result, at least some of the known systems are fed with data including this deterministic trend that influences the training stage of the known systems. Overall, many of the known systems are limited for the prediction of specific types of securities and data, such as the price of a single stock and, thus, cannot be universally applied to any financial time series, price series and volatility series.
It is, therefore, desirable to provide a prediction system based on the recurrent Artificial Neural Network (ANN) architecture which is able to output upper and lower predictions bounds at any given confidence level. Also, an ANN prediction that can be applied to financial time series, price series and volatility series, for single securities and for portfolios of securities is desirable. A universal prediction system employing a pipeline recurrent neural network (PRNN), which provides the satisfactory accuracy of the nonlinear and adaptive prediction of nonstationary signal and time series processes is also desirable. Further, a universal ANN prediction system having high computation efficiency and multi-stage adaptive supervised training process is also desirable.
Accordingly, an inventive universal ANN prediction system motivated in its design by the human nervous system is capable of learning by training to generalize from special cases and outputting a three-line band to forecast shortterm movements of stock prices.
Referring to one aspect of the invention, a supervised training and prediction system is so trained that the online investors are presented with the forecast of short-term movements of stock prices in a scientific way. Particularly, a three-line presentation that defines a confidence range or level, within which a price stock is predicted to fluctuate, enables the online investors to obtain a good understanding of the possible stock prices in a probability sense.
Such presentation becomes possible due a training procedure of a ANN prediction system in accordance with the invention. Particularly, a training set, which includes the input data for the ANN to xe2x80x9cseexe2x80x9d and the known target data for ANN to learn to output, is first collected. For stock price predictions, for example, the training set and target data would naturally be historical stock prices. A vector of 100 consecutive historical stock prices, for instance, can constitute a training data and the 101st stock price can be a target datum. The inventive process further is characterized by feeding the input data to ANN; compare ANN output with the known target, and adjust ANN""s internal parameters (weights and biases) so that ANN output and the known target are close to one anotherxe2x80x94more precisely, so that a certain error function is minimized. Further, the process is characterized by feeding ANN some future input data (not seen by ANN); if ANN is well trained and if the input data is predictable, then ANN will give accurate predictions.
The inventive system is essentially an Artificial Neural Network trained for adaptive prediction of stock prices. During the prediction process, the inventive system (TradetrekTm Neuro-Predictorm) determines whether a particular stock is predictable with the accuracy required for a statistically significant prediction. This is accomplished, essentially, by comparing the ANN validation error against stock price fluctuations. We know that stocks with larger chaotic components and smaller truly random components tend to be more predictable than others.
In accordance with still another aspect of the invention, the deterministic or expected trend of the chaotic component of a signal representing the evaluated time-series data is determined in accordance with log-linear chisquared linear least squares based on the Black-Scholes stock price formula. The Black-Scholes formula, or other option pricing formula, is used to determine expected option costs in determining necessary hedging and pricing. The Black-Scholes formula provides an option cost based upon index price, exercise price, option term and assumptions of risk free rates of return, average dividend yield, and volatility of returns (standard deviation of returns). The trend is removed before feeding the data to the ANN engine and added back to the data in the post processing stage of the inventive process.
Overall, the simplified ANN (supervised) training and prediction process can be illustrated by the following steps.
Stage One:
Collect the training set, which includes the input data for the ANN to xe2x80x9cseexe2x80x9d and the known target data for ANN to learn to output. For stock price predictions, for example, the training set and target data would naturally be historical stock prices. A vector of 100 consecutive historical stock prices, for instance, can constitute training data and with the 101st stock price as a target datum.
Stage Two:
Feed the input data to ANN; compare ANN output with the known target, and adjust ANN""s internal parameters (weights and biases) so that ANN output and the known target are close to one anotherxe2x80x94more precisely, so that a certain error function is determined and further minimized.
Step Three:
Feed ANN some future input data (not seen by ANN); if ANN is well trained and if the input data are predictable, then ANN will give accurate predictions.
The inventive system has managed to yield prediction refinements well beyond those of other systems by employing a pipelined recurrent ANN architecture (best for time-series prediction) and an adaptive supervised training procedure.
It is, accordingly, an object of the present invention to provide an artificial neural network system operating with a determined error function for data processing and predicting stock prices at a given confidence level.
It is yet another object of the present invention to develop a process for stock prediction on the basis of evaluation of the collected data using a neural network system.
Yet another object of the present invention is to provide a data processing system based on an artificial neural network employing a pipelined recurrent ANN architecture to provide the satisfactory accuracy of the nonlinear and adaptive prediction of time series process.
A further object of the invention relates to a component object module (COP) technique allowing any COM support computer languages and applications to call the inventive prediction system.